Characteristics of a ceramic-substrate x-ray diode and its application to computed tomography

X-ray photons are detected using a Lu2(SiO4)O [LSO] single-crystal scintillator with a decay time of 40 ns and a multipixel photon counter (MPPC). The photocurrent from the MPPC is amplified by a high-speed current–voltage amplifier with an 80 MHz-gain-band operational amplifier, and the 200-ns-width event pulses are sent to a multichannel analyzer (MCA) to measure X-ray spectra. The MPPC was driven in the pre-Geiger mode at a bias voltage of 70.7 V and a temperature of 23 °C. Photon-counting computed tomography (PC-CT) is accomplished by repeated linear scans and rotations of an object, and projection curves of the object are obtained by linear scanning at a tube current of 1.5 mA. In PC-CT, the event pulse height is dispersed using a 7-ns-delay comparator. The exposure time for obtaining a tomogram is 10 min with scan steps of 0.5 mm and rotation steps of 1.0°. At a tube voltage of 80 kV, the maximum count rate is 250 kcps. We carry out PC-CT using iodine media and confirm the energy-dispersive effect with changes in the lower level voltage of event pulses using a comparator.

As a routine preoperative evaluation for chronic sinusitis and postoperative maxillary cyst (mucocele and pyocele), CT scans and B-mode ultrasonic diagnosis including three methods, linear, sector, and convex scans, were performed. By chronic sinusitis, 112 sides by linear scans, 43 sides by sector scans, and 29 sides by convex scans were included in the study. By the postoperative cyst, 43 sides by linear scans, 24 sides by sector scans, and 12 sides by convex scans were included. The positive diagnostic finding by ultrasonography is defined as the depicted echo of the posterior wall. In chronic sinusitis, positive diagnostic rates were as follows: linear scans, 73.2%, sector scans, 88.2%, and convex scans, 77.8%. As for the scan planes, horizontal plane yielded more positive than sagittal one. In postoperative cyst similarly, linear, 86.0%, sector, 95.8%, and convex, 83.3%. Horizontal more positive than sagittal also. The negative findings were caused, in chronic sinusitis, by attenuation by the air in the sinus, thick anterior wall, and artifacts. In postoperative cysts, the negative findings were caused by specific location of the cysts (lateral or posterior). Our results recommend the use of sector scan because of its easy manipulation and higher positive diagnostic rates due to the radial beam conforming to the curvature of the posterior wall. Especially, the sector scan was higher diagnostic rate than linear scan, because the sector scan transmitted radial beam, and received its reflective wave easily from a curved posterior wall of the maxillary sinus. The sector scan, its manipulation is easy, and its diagnostic rate is higher than any other scans.

Objective. Photon counting CT (PCCT) has been a research focus in the last two decades. Recent studies and advancements have demonstrated that systems using semiconductor-based photon counting detectors (PCDs) have the potential to provide better contrast, noise and spatial resolution performance compared to conventional scintillator-based systems. With multi-energy threshold detection, PCD can simultaneously provide the photon energy measurement and enable material decomposition for spectral imaging. In this work, we report a performance evaluation of our first CdZnTe-based prototype full-size PCCT system through various phantom imaging studies. Approach. This prototype system supports a 500 mm scan field-of-view and 10 mm z-coverage at isocenter. Phantom scans were acquired using 120 kVp from 50 to 400 mAs to assess the imaging performance on: CT number accuracy, uniformity, noise, spatial resolution, material differentiation and quantification. Main results. Both qualitative and quantitative evaluations show that PCCT, under the tested conditions, has superior imaging performance with lower noise and improved spatial resolution compared to conventional energy integrating detector (EID)-CT. Using projection domain material decomposition approach with multiple energy bin measurements, PCCT virtual monoenergetic images have lower noise, and good accuracy in quantifying iodine and calcium concentrations. These results lead to increased contrast-to-noise ratio (CNR) for both high and low contrast study objects compared to EID-CT at matched dose and spatial resolution. PCCT can also generate super-high resolution images using much smaller detector pixel size than EID-CT and greatly improve image spatial resolution. Significance. Improved spatial resolution and quantification accuracy with reduced image noise of the PCCT images can potentially lead to better diagnosis at reduced radiation dose compared to conventional EID-CT. Increased CNR achieved by PCCT suggests potential reduction in iodine contrast media load, resulting in better patient safety and reduced cost.

Photon-counting computed tomography (PCCT) is a promising new technology with the potential to fundamentally change today's workflows in the daily routine and to provide new quantitative imaging information to improve clinical decision-making and patient management. The content of this review is based on an unrestricted literature search on PubMed and Google Scholar using the search terms "Photon-Counting CT", "Photon-Counting detector", "spectral CT", "Computed Tomography" as well as on the authors' experience. The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCCT allows counting of every single photon at the detector level. Based on the identified literature, PCCT phantom measurements and initial clinical studies have demonstrated that the new technology allows improved spatial resolution, reduced image noise, and new possibilities for advanced quantitative image postprocessing. For clinical practice, the potential benefits include fewer beam hardening artifacts, radiation dose reduction, and the use of new contrast agents. In this review, we will discuss basic technical principles and potential clinical benefits and demonstrate first clinical use cases. · Photon-counting computed tomography (PCCT) has been implemented in the clinical routine. · Compared to energy-integrating detector CT, PCCT allows the reduction of electronic image noise. · PCCT provides increased spatial resolution and a higher contrast-to-noise ratio. · The novel detector technology allows the quantification of spectral information. · Stein T, Rau A, Russe MF et al. Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience. Fortschr Röntgenstr 2023; 195: 691 - 698.

Recently, photon-counting computed tomography (PCCT) has been extensively studied as a promising modality for spectral imaging. PCCT offers some benefits over conventional CT, such as energy-resolved X-ray information and reduced radiation dose owing to its ability to count and discriminate each X-ray photon based on its energy. However, PCCT images in certain energy bands tend to suffer from artifacts and image noise owing to insufficient photon counts. These problems reduce the accuracy and quality of PCCT images, thereby limiting the full potential of energy-resolved imaging. Recently, many studies have been conducted on the application of deep learning to conventional CT for the reduction of image noise and other improvements, which are considered promising. However, the application of deep learning to PCCT has not been thoroughly investigated. In this study, we investigated the effectiveness of deep learning in improving PCCT images and explored a new approach for utilizing energy-resolved PCCT information. For this purpose, we conducted experiments using our PCCT system and acquired data by scanning phantoms and mice. Deep learning was then applied to the data, and the images were evaluated in terms of noise and material-decomposed imaging. Consequently, image noise and artifacts are significantly suppressed by the application of deep learning. In particular, more effective noise reduction was achieved with a model that could refer to and utilize energy information. Improvements were also confirmed in the accuracy of contrast agent discrimination in phantom imaging, as well as in the visualization of accumulated contrast agents in organs in mouse imaging. Consequently, deep learning effectively incorporating energy information was shown to significantly improve the PCCT image quality, demonstrating a new potential for energy information in PCCT.

Demonstration of multiple contrast agent imaging for the next generation color X-ray CT

Flow diverters are a crucial element in the treatment of intracranial aneurysms. However, the optimal non-invasive follow-up imaging modality, particularly for the detection of in-stent stenosis, remains uncertain. This study aims to compare the performance of photon-counting detector CT (PCD-CT) in ultra-high-resolution (UHR) mode with flat-panel CT (FP-CT) for the evaluation of intracranial flow diverters. A phantom model for intracranial vessels was used to evaluate 15 flow diverters of various sizes and designs. Imaging was performed using both PCD-CT and FP-CT. Qualitative assessment of the stent lumen was conducted by three experienced neuroradiologists using a 5-point Likert scale. Quantitative analysis included measurements of lumen area, contrast to noise ratio and signal to noise ratio. FP-CT provided a significantly larger assessable stent lumen than PCD-CT at all dose levels (p < 0.05), with no significant differences between PCD-CT dose levels (p = 0.999). Increasing PCD-CT dose did not improve lumen visualization. SNR and CNR increased with PCD-CT dose (p < 0.001), peaking at CTDI 20, but showed diminishing returns beyond CTDI 10. Flow diverter diameter correlated positively with SNR and CNR (p < 0.05). Subjective image quality improved with PCD-CT dose (p < 0.001) but showed no significant difference beyond 10 mGy (p > 0.05). FRED devices had the lowest ratings, independent of imaging modality (p = 0.80). Our study demonstrated that while FP-CT provided superior visualization of the flow diverter lumen in a head phantom vessel model, subjective assessability ratings were comparable between FP-CT and PCD-CT when evaluated by experienced readers. PCD-CT at a CTDIvol of 10 mGy offered the best balance between image quality and radiation dose, making it a viable alternative for post-interventional assessment of flow diverters.

Detailed visualization of bone microarchitecture is essential for assessment of wrist fractures in computed tomography (CT). This study aims to evaluate the imaging performance of a CT system with clinical cadmium telluride-based photon-counting detector (PCD-CT) compared with a third-generation dual-source CT scanner with energy-integrating detector technology (EID-CT). Both CT systems were used for the examination of 8 cadaveric wrists with radiation dose equivalent scan protocols (low-/standard-/full-dose imaging: CTDIvol = 1.50/5.80/8.67 mGy). All wrists were scanned with 2 different operating modes of the photon-counting CT (standard-resolution and ultra-high-resolution). After reformatting with comparable reconstruction parameters and convolution kernels, subjective evaluation of image quality was performed by 3 radiologists on a 7-point scale. For estimation of interrater reliability, we report the intraclass correlation coefficient (absolute agreement, 2-way random-effects model). Signal-to-noise and contrast-to-noise ratios were calculated to provide semiquantitative assessment of image quality. Subjective image quality of standard-dose PCD-CT examinations in ultra-high-resolution mode was superior compared with full-dose PCD-CT in standard-resolution mode (P = 0.016) and full-dose EID-CT (P = 0.040). No difference was ascertained between low-dose PCD-CT in ultra-high-resolution mode and standard-dose scans with either PCD-CT in standard-resolution mode (P = 0.108) or EID-CT (P = 0.470). Observer evaluation of standard-resolution PCD-CT and EID-CT delivered similar results in full- and standard-dose scans (P = 0.248/0.509). Intraclass correlation coefficient was 0.876 (95% confidence interval, 0.744-0.925; P < 0.001), indicating good reliability. Between dose equivalent studies, signal-to-noise and contrast-to-noise ratios were substantially higher in photon-counting CT examinations (all P's < 0.001). Superior visualization of fine anatomy is feasible with the clinical photon-counting CT system in cadaveric wrist scans. The ultra-high-resolution scan mode suggests potential for considerable dose reduction over energy-integrating dual-source CT.

PurposeThe purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. Materials and methodsConsecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. ResultsA total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51–93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0–98.7), 68.7% (95% CI: 60.1–76.4), 74.3 % (95% CI: 69.1–78.8), 94.8% (95% CI: 88.5–97.8), and 81.9% (95% CI: 76.7–86.4) for PC-CCTA, and 96.8% (95% CI: 92.1–99.1), 87.3% (95% CI: 80.5–92.4), 87.8% (95% CI: 82.2–91.8), 96.7% (95% CI: 91.7–98.7), and 91.9% (95% CI: 87.9–94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88–0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77–0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). ConclusionDeep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

An energy-discrimination K-edge x-ray computed tomography (CT) system is useful for controlling the image contrast of a target region by selecting both the photon energy and the energy width. The CT system has an oscillation-type linear cadmium telluride (CdTe) detectror. CT is performed by repeated linear scans and rotations of an object. Penetrating x-ray photons from the object are detected by a CdTe detector, and event signals of x-ray photons are produced using charge-sensitive and shaping amplifiers. Both photon energy and energy width are selected out using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, the tube voltage and tube current were 80 kV and 20 &mu;A, respectively, and the x-ray intensity was 1.92 &mu;Gy/s at a distance of 1.0 m from the source and a tube voltage of 80 kV. The energy-discrimination CT was carried out by selecting x-ray photon energies.

Photon-counting computed tomography (PCCT) is a significant technological advancement in musculoskeletal imaging. Unlike traditional CT detectors, which are energy-integrating detectors, PCCT uses direct-conversion technology, or photon-counting detectors. This enables ultra-high spatial resolution, systematic spectral imaging, and effective electronic noise reduction without increasing radiation exposure. This review article illustrates the potential benefit of PCCT in clinical practice across a broad spectrum of musculoskeletal disorders. PCCT is expected to improve the detection and characterization of fractures, infections, inflammatory and degenerative arthropathies, bone marrow disorders, tumors, congenital bone diseases, and postoperative complications. It will also assist with interventional procedures. PCCT holds great promise for opportunistic imaging and artificial intelligence-driven analytics in musculoskeletal radiology.

&#xD;Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. &#xD;&#xD;Approach. &#xD;We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. &#xD;&#xD;Main results. &#xD;Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). &#xD;&#xD;Significance. &#xD;Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Application of an oscillation-type linear cadmium telluride detector to enhanced gadolinium K-edge computed tomography

Objectives:To evaluate the clinical performance of a cadmium-zinc-telluride- (CZT-) based photon-counting computed tomography (PCCT) system for low-dose lung cancer screening (LCS-LDCT) using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system.Methods:Six 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-glass), were imaged on PCCT and EIDCT scanners at matched dose levels (1.6 – 20.4 mGy). Quantitative image metrics, Hounsfield unit (HU) accuracy, image noise, and contrast-to-noise ratio (CNR), were assessed across dose levels. Radiomic features were extracted for each lesion and analyzed via principal component analysis to quantify feature consistency (within-cluster distance) and lesion type separability.Results:PCCT demonstrated significantly lower image noise and higher CNR compared with EIDCT, particularly at lower dose levels. HU values were consistent across doses for both systems, with reduced variability in PCCT (coefficient of variation < 0.004). Radiomics analysis revealed tighter clustering (reduced within-cluster distances) and comparable lesion type separability between PCCT and EIDCT, indicating enhanced feature stability and lesion differentiation. Qualitative review confirmed superior lesion conspicuity and margin delineation with PCCT.Conclusions:CZT-based PCCT outperforms conventional EIDCT in quantitative and qualitative imaging metrics for LCS-LDCT, enabling superior image quality and radiomics reproducibility at reduced radiation doses. These findings support the clinical translation of PCCT for lung cancer screening and radiomics-based lesion characterization.